CN116430347B - Radar data acquisition and storage method - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/414—Discriminating targets with respect to background clutter
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The invention provides a radar data acquisition and storage method, which belongs to the technical field of radar detection targets.
Description
Technical Field
The invention relates to the technical field of radar detection targets, in particular to a radar data acquisition and storage method.
Background
Radar is used for radio detection and ranging, finding a target by using a radio method, and obtaining information of the target. Radar is an electronic device that detects a target using electromagnetic waves. The radar emits electromagnetic waves to irradiate the target and receives echoes of the target, the received echoes contain target information, and the target information is obtained by analyzing the echo information. But the returned echo also contains noise and interference signals, and the noise and the interference signals influence the extraction of the target information.
Disclosure of Invention
Aiming at the defects in the prior art, the radar data acquisition and storage method provided by the invention solves the problems of noise and interference signals in the reflected signals received by the radar receiving unit.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a radar data acquisition and storage method comprises the following steps:
s1, transmitting signals through a radar transmitting unit;
s2, transmitting signals through target reflection, and receiving the reflected signals through a radar receiving unit;
s3, denoising and interference removal processing is carried out on the reflected signals, and effective receiving signals are obtained;
s4, extracting target characteristics according to the effective received signals and the emission signals;
s5, carrying out encryption distributed storage on the target characteristics.
Further, the expression of the transmission signal in S1 is:
wherein ,for transmitting signals +.>For the amplitude of the transmitted signal, +.>For the base frequency +.>For the number of array elements, < > for>For frequency deviation>For the phase of the transmitted signal>For time (I)>For the number of array elements, < > for>As a cosine function.
Further, the expression of the reflected signal received in S2 is:
wherein ,for the received reflected signal +.>For transmitting signals +.>For the target matrix +.>For interfering signals +.>Is a noise signal.
Further, the step S3 includes the following sub-steps:
s31, establishing a denoising and interference elimination model;
s32, transmitting signals to a known target through a radar transmitting unit to obtain theoretical received signals and actual received signals;
s33, constructing a theoretical received signal and an actual received signal into a training set;
s34, training a denoising and interference elimination model by adopting a training set, and calculating an error during training;
s35, judging whether the error is lower than an error threshold, if so, finishing the training of the denoising and interference elimination model, otherwise, adjusting the weight and the bias of the denoising and interference elimination model according to the error, and jumping to S34;
s36, inputting the reflected signals into a trained denoising and interference elimination model to obtain effective received signals.
The beneficial effects of the above further scheme are: the invention constructs the denoising and interference removal process as a model, and achieves the aim of denoising and interference removal at the same time. The invention transmits signals to the known target through the radar transmitting unit, the target characteristics of the known target are known, so that the theoretical receiving signals can be directly obtained, the actual receiving signals and the theoretical receiving signals can be constructed as training samples, the model relation between the actual receiving signals and the theoretical receiving signals is established, and the interference signals are removedAnd noise signal->Without exploring the interference signal +.>And noise signal->And particularly, the influence on the actual received signal is realized, and the rapid denoising and interference removal are realized.
Further, the denoising and interference elimination model in S31 is as follows:
wherein ,for the actual output of the denoising and interference removal model, +.>For actually receiving the signal, +.>As a function of the hyperbolic tangent,as a logarithmic function>Is natural constant (18)>For the first weight, ++>For the first bias->For the second weight, ++>For the second bias->Is a cache parameter.
The beneficial effects of the above further scheme are: the denoising and interference elimination model of the invention comprises two layers of weights and offsets, and when the weights and offsets of the first layer are used, the invention adopts a logarithmic function to strengthen the characteristics of the input actual received signals, thereby facilitating the hyperbolic tangent functionNormalized with the actual received signal>Multiplying to establish the actual received signal +.>To the cache parameter->The invention adopts the second weight and bias to establish the buffer parameter +.>Relation to actual output->The relation between the input and the output is expressed through the weight and the bias of the two layers, so that the model has better denoising and interference removing effects.
Further, the calculation formula of the error in S34 is:
wherein ,is->Error during training->The>Actual output during secondary training, +.>Is->Theoretical received signal during secondary training, +.>For the number of training times, the user is strapped>As a fraction coefficient +.>For the number of training times, ∈>Is the number of training times.
The beneficial effects of the above further scheme are: the invention adopts the actual output when training for a plurality of timesAnd theoretical received signal->Difference, and actual output ∈>And theoretical received signal->Is integrated with the error condition by actually outputting +.>And theoretical received signal->The difference value only represents the difference between the two values, and the difference value cannot represent the similarity degree. According to the invention, the difference of multiple training is considered to obtain the error, so that the model has smaller error on the whole during training, and high-precision denoising and interference elimination are realized.
Further, the formula for adjusting the weight of the denoising and interference elimination model in S35 is as follows:
the formula for adjusting the bias of the denoising and interference elimination model is as follows:
wherein ,is->Weight during secondary training, +.>Is->Weight during secondary training, +.>Is->Error during training->Is->Bias during secondary training->Is->Bias during secondary training->For partial derivative operation, < ->For maximum training times, +.>As a cosine function.
The beneficial effects of the above further scheme are: the weight and bias descending degree of the invention depends on the error and the training times, and the error is larger and the training times are smaller in the initial training period, so the weight and bias are iterated rapidly, the error is smaller, the training times are more, the weight and bias are slowly descended in the later training period, the model output is approximate to the ideal value, and the self-adaptive adjustment of the output is realized.
Further, the step S4 includes the following sub-steps:
s41, establishing a receiving and transmitting relation according to the effective receiving signals and the transmitting signals to obtain a target matrix;
s42, decomposing the characteristic value of the target matrix to obtain the characteristic value;
s43, constructing the characteristic value as a vector to obtain the target characteristic.
The beneficial effects of the above further scheme are: after the denoising and interference elimination model, no interference signal exists in the receiving and transmitting relationAnd noise signal->Solving a target matrix, and then carrying out eigenvalue decomposition on the target matrix to obtain eigenvalues, wherein the eigenvalues represent target information.
Further, the receiving and transmitting relation in S41 is:
wherein ,for effective reception of signals, < >>For transmitting signals +.>Is a target matrix.
Further, the encryption formula in S5 is:
wherein ,for the encrypted target feature +.>For the object feature->The number of characteristic values in>As a logarithmic function>Is natural constant (18)>For the object feature->Storage location (s)/(s)>For encryption coefficients>For encryption weight, ++>Is a rounding operation.
The beneficial effects of the above further scheme are: after the target characteristics are obtained, the invention carries out encryption storage, and the target characteristics are encryptedStorage location->And target feature->Number of characteristic values ∈>Is integrated into the encryption formula, and simultaneously, encryption weight is set>And encryption coefficient->As the adjustment quantity, the encryption randomness is convenient to increase, and the data can be stored better.
The technical scheme of the embodiment of the invention has at least the following advantages and beneficial effects: according to the method, the radar transmitting unit transmits signals, the unknown target reflects the transmitted signals, the radar receiving unit receives the reflected signals, the received reflected signals are subjected to denoising and interference elimination processing, effective received signals are extracted, target characteristics are extracted according to the relation between the effective received signals and the transmitted signals, and the target characteristics are stored in an encryption distributed mode, so that the target information is not lost.
Drawings
Fig. 1 is a flow chart of a radar data acquisition and storage method.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
As shown in fig. 1, a radar data acquisition and storage method includes the following steps:
s1, transmitting signals through a radar transmitting unit;
the expression of the emission signal in the S1 is as follows:
wherein ,for transmitting signals +.>For the amplitude of the transmitted signal, +.>For the base frequency +.>For the number of array elements, < > for>For frequency deviation>For the phase of the transmitted signal>For time (I)>For the number of array elements, < > for>As a cosine function.
S2, transmitting signals through target reflection, and receiving the reflected signals through a radar receiving unit;
the expression of the reflected signal received in S2 is:
wherein ,for the received reflected signal +.>For transmitting signals +.>For the target matrix +.>For interfering signals +.>Is a noise signal.
S3, denoising and interference removal processing is carried out on the reflected signals, and effective receiving signals are obtained;
the step S3 comprises the following substeps:
s31, establishing a denoising and interference removal model:
wherein ,for the actual output of the denoising and interference removal model, +.>For actually receiving the signal, +.>As a function of the hyperbolic tangent,as a logarithmic function>Is natural constant (18)>For the first weight, ++>For the first bias->For the second weight, ++>For the second bias->Is a cache parameter.
The denoising and interference elimination model of the invention comprises two layers of weights and offsets, and when the weights and offsets of the first layer are used, the invention adopts a logarithmic function to strengthen the characteristics of the input actual received signals, thereby facilitating the hyperbolic tangent functionNormalized with the actual received signal>Multiplying to establish the actual received signal +.>To the cache parameter->The invention adopts the second weight and bias to establish the buffer parameter +.>Relation to actual output->The relation between the input and the output is expressed through the weight and the bias of the two layers, so that the model has better denoising and interference removing effects.
S32, transmitting signals to a known target through a radar transmitting unit to obtain theoretical received signals and actual received signals;
s33, constructing a theoretical received signal and an actual received signal into a training set;
s34, training a denoising and interference elimination model by adopting a training set, and calculating an error during training;
the actual received signal is used as the input of the denoising and interference elimination model during training, and the theoretical received signal is used as the standard for adjusting the weight and the bias.
The calculation formula of the error in S34 is:
wherein ,is->Error during training->The>Actual output during secondary training, +.>Is->Theoretical received signal during secondary training, +.>For the number of training times, the user is strapped>As a fraction coefficient +.>For the number of training times, ∈>Is the number of training times.
The invention adopts the actual output when training for a plurality of timesAnd theoretical received signal->Difference, and actual output ∈>And theoretical received signal->Is integrated with the error condition by actually outputting +.>And theoretical received signal->The difference value only represents the difference between the two values, and the difference value cannot represent the similarity degree. According to the invention, the difference of multiple training is considered to obtain the error, so that the model has smaller error on the whole during training, and high-precision denoising and interference elimination are realized.
S35, judging whether the error is lower than an error threshold, if so, finishing the training of the denoising and interference elimination model, otherwise, adjusting the weight and the bias of the denoising and interference elimination model according to the error, and jumping to S34;
the formula for adjusting the weight of the denoising and interference elimination model in the step S35 is as follows:
the formula for adjusting the bias of the denoising and interference elimination model is as follows:
wherein ,is->Weight during secondary training, +.>Is->Weight during secondary training, +.>Is->Error during training->Is->Bias during secondary training->Is->Bias during secondary training->For partial derivative operation, < ->For maximum training times, +.>As a cosine function.
The weight and bias descending degree of the invention depends on the error and the training times, and the error is larger and the training times are smaller in the initial training period, so the weight and bias are iterated rapidly, the error is smaller, the training times are more, the weight and bias are slowly descended in the later training period, the model output is approximate to the ideal value, and the self-adaptive adjustment of the output is realized.
S36, inputting the reflected signals into a trained denoising and interference elimination model to obtain effective received signals.
The invention constructs the denoising and interference removal process as a model, and achieves the aim of denoising and interference removal at the same time. The invention transmits signals to the known target through the radar transmitting unit, the target characteristics of the known target are known, so that the theoretical receiving signals can be directly obtained, the actual receiving signals and the theoretical receiving signals can be constructed as training samples, the model relation between the actual receiving signals and the theoretical receiving signals is established, and the interference signals are removedAnd noise signal->Without exploring the interference signal +.>And noise signal->And particularly, the influence on the actual received signal is realized, and the rapid denoising and interference removal are realized.
S4, extracting target characteristics according to the effective received signals and the emission signals;
the step S4 comprises the following substeps:
s41, establishing a receiving and transmitting relation according to the effective receiving signals and the transmitting signals to obtain a target matrix;
the receiving and transmitting relation in S41 is:
wherein ,for effective reception of signals, < >>For transmitting signals +.>Is a target matrix.
S42, decomposing the characteristic value of the target matrix to obtain the characteristic value;
s43, constructing the characteristic value as a vector to obtain the target characteristic.
After the invention passes through the denoising and interference elimination model, no interference signal exists in the receiving and transmitting relationAnd noise signalSolving a target matrix, and then carrying out eigenvalue decomposition on the target matrix to obtain eigenvalues, wherein the eigenvalues represent target information.
S5, carrying out encryption distributed storage on the target characteristics.
The encryption formula in S5 is as follows:
wherein ,for the encrypted target feature +.>For the object feature->The number of characteristic values in>As a logarithmic function>Is natural constant (18)>For the object feature->Storage location (s)/(s)>For encryption coefficients>For encryption weight, ++>Is a rounding operation.
After the target characteristics are obtained, the invention carries out encryption storage, and the target characteristics are encryptedStorage location->And target feature->Number of characteristic values ∈>Is integrated into the encryption formula, and simultaneously, encryption weight is set>And encryption coefficient->As the adjustment quantity, the encryption randomness is convenient to increase, and the data can be stored better.
The distributed storage model in the invention is as follows:
wherein ,in the +.>Storing target features on a table device>Is (are) located>In order to activate the function in the shape of an S,for hyperbolic tangent activation function,/->Is natural constant (18)>For rounding operations, ++>For the remainder operation, ++>Is the number of the storage device.
In the distributed storage, the storage position is determined by the position calculated by the distributed storage model, and the storage positions of different storage devices are different, so that the random storage method also has the randomness in the storage position, and the data is safer. And rounding operation is adopted in the encryption or storage process, so that the target data can be restored conveniently.
The technical scheme of the embodiment of the invention has at least the following advantages and beneficial effects: according to the method, the radar transmitting unit transmits signals, the unknown target reflects the transmitted signals, the radar receiving unit receives the reflected signals, the received reflected signals are subjected to denoising and interference elimination processing, effective received signals are extracted, target characteristics are extracted according to the relation between the effective received signals and the transmitted signals, and the target characteristics are stored in an encryption distributed mode, so that the target information is not lost.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The radar data acquisition and storage method is characterized by comprising the following steps:
s1, transmitting signals through a radar transmitting unit;
s2, transmitting signals through target reflection, and receiving the reflected signals through a radar receiving unit;
s3, denoising and interference removal processing is carried out on the reflected signals, and effective receiving signals are obtained;
s4, extracting target characteristics according to the effective received signals and the emission signals;
s5, carrying out encryption distributed storage on the target characteristics;
the step S3 comprises the following substeps:
s31, establishing a denoising and interference elimination model;
s32, transmitting signals to a known target through a radar transmitting unit to obtain theoretical received signals and actual received signals;
s33, constructing a theoretical received signal and an actual received signal into a training set;
s34, training a denoising and interference elimination model by adopting a training set, and calculating an error during training;
s35, judging whether the error is lower than an error threshold, if so, finishing the training of the denoising and interference elimination model, otherwise, adjusting the weight and the bias of the denoising and interference elimination model according to the error, and jumping to S34;
s36, inputting the reflected signals into a trained denoising and interference elimination model to obtain effective received signals;
the denoising and interference removal model in S31 is as follows:
wherein ,for the actual output of the denoising and interference removal model, +.>For actually receiving the signal, +.>As hyperbolic tangent function, +.>As a logarithmic function>Is natural constant (18)>For the first weight, ++>For the first bias->For the second weight, ++>For the second bias->Is a cache parameter.
2. The method for collecting and storing radar data according to claim 1, wherein the expression of the transmitted signal in S1 is:
wherein ,for transmitting signals +.>For the amplitude of the transmitted signal, +.>For the base frequency +.>For the number of array elements, < > for>For the frequency offset to be a frequency offset,for the phase of the transmitted signal>For time (I)>For the number of array elements, < > for>As a cosine function.
3. The method for collecting and storing radar data according to claim 1, wherein the expression of the reflected signal received in S2 is:
wherein ,for the received reflected signal +.>For transmitting signals +.>For the target matrix +.>For interfering signals +.>Is a noise signal.
4. The method for collecting and storing radar data according to claim 1, wherein the calculation formula of the error in S34 is:
wherein ,is->Error during training->The>Actual output during secondary training, +.>Is->Theoretical received signal during secondary training, +.>For the number of training times, the user is strapped>As a fraction coefficient +.>For the number of training times, ∈>Is the number of training times.
5. The method for collecting and storing radar data according to claim 1, wherein the formula for adjusting the weight of the denoising and interference elimination model in S35 is:
the formula for adjusting the bias of the denoising and interference elimination model is as follows:
wherein ,is->Weight during secondary training, +.>Is->Weight during secondary training, +.>Is->Error during training->Is->Bias during secondary training->Is->Bias during secondary training->For partial derivative operation, < ->For maximum training times, +.>As a cosine function.
6. The method for collecting and storing radar data according to claim 1, wherein S4 comprises the following sub-steps:
s41, establishing a receiving and transmitting relation according to the effective receiving signals and the transmitting signals to obtain a target matrix;
s42, decomposing the characteristic value of the target matrix to obtain the characteristic value;
s43, constructing the characteristic value as a vector to obtain the target characteristic.
7. The method for collecting and storing radar data according to claim 6, wherein the receiving and transmitting relation in S41 is:
wherein ,for effective reception of signals, < >>For transmitting signals +.>Is a target matrix.
8. The method for collecting and storing radar data according to claim 1, wherein the encryption formula in S5 is:
wherein ,for the encrypted target feature +.>For the object feature->The number of characteristic values in>As a logarithmic function>Is natural constant (18)>For the object feature->Storage location (s)/(s)>For encryption coefficients>For encryption weight, ++>Is a rounding operation.
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